code: def __init__(
      self,
      coreml_model,  # type: MLModel
      onnx_outputs_info,  # type: Dict[Text, EdgeInfo]
      useCPUOnly=False,  # type: bool
      minimum_ios_deployment_target="12",  # type: str
  ):
      # type: (...) -> None
      super(CoreMLRep, self).__init__()
      self.model = coreml_model
      self.useCPUOnly = useCPUOnly
      self.minimum_ios_deployment_target = minimum_ios_deployment_target
  
      spec = coreml_model.get_spec()
      self.input_names = [str(i.name) for i in spec.description.input]
      self.output_names = [str(o.name) for o in spec.description.output]
      self.onnx_outputs_info = onnx_outputs_info
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/onnx/_backend_rep.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from typing import Any, Sequence, List
  from onnx.backend.base import BackendRep, namedtupledict
  from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
  from coremltools.proto import FeatureTypes_pb2
  from coremltools.models import MLModel
  from typing import Dict, Any, Text, Tuple
  from onnx import TensorProto
  from ._graph import EdgeInfo
  from ._converter import SupportedVersion
label_desc: Use image node_id 1 to create a new CoreMLRep object from inherited base classes: BackendRep with example: obj = CoreMLRep(coreml_model, onnx_outputs_info, useCPUOnly, minimum_ios_deployment_target)